Role Summary :

Design, build, lead, and deliver production-grade AI solutions on Azure/ AWS/GCP. Own execution excellence with measurable business value, technical depth, governance, and reliability.

Key Outcomes (0612 months) :

- Ship production-grade AI/GenAI solutions with clear ROI, reliability (SLOs), and security.

- Establish engineering standards, CI/CD pipelines, observability, and repeatable delivery patterns.

- Build a reusable AI platform that enables AI applications across multiple domains (paved paths, templates, guardrails).

- Mentor engineers via reviews, playbooks, and hands-on guidance.

Responsibilities :

- Translate business problems into well-posed technical specifications and architectures.

- Lead design reviews, prototype quickly, and harden solutions for scale (high QPS / 1M+ users).

- Build automated pipelines (CI/CD) and model/data governance across environments (dev/test/prod).

- Define and track KPIs: accuracy, latency, cost, adoption, and compliance readiness.

- Partner with Product, Security, Compliance, and Ops to land safe-by-default systems.

GenAI + Agentic AI on Azure (must-have focus) :

- Implement Azure OpenAI solutions (prompting, evals, fine-tuning where applicable, safety filters).

- Build RAG architectures using Azure AI Search (vector) + curated data sources (SharePoint, SQL, Blob/ADLS, APIs).

- Design agentic workflows (tool use, multi-step orchestration, human-in-the-loop) using combinations of:

1. Azure Functions / Durable Functions, Logic Apps, Event Grid, Service Bus

2. Frameworks like Semantic Kernel / LangChain (as orchestration layer)

- Implement observability for agent workflows (traces, latency breakdown, failure modes, cost per run).